Abstract
Most skimming devices attached to an automatic teller machine (ATM) are similar in color and shape to the host machine, vision-based detection of such things is therefore difficult. A background subtraction method may be used to detect changes in a normal situation. However, without human detection, its background model is sometimes polluted by the ATM user, and the method cannot detect suspicious objects left in the scene. This paper proposes a real-time system which integrates (i) a simple image subtraction for detection of user arrival and departure, and (ii) an automatic detection of suspicious objects left on the ATM. The background model is updated only when no user is found, and used to detect suspicious objects based on a guided adaptive threshold. To avoid a detection miss, nonlinear enhancement is applied to amplify the intensity differences between foreign objects and host machine. Experimental results show that the proposed system increases correctly detected area by 13.21% compared with the fixed threshold method. It has no detection miss and false alarm either.
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References
Arabo, A.: Secure cash withdrawal through mobile phone/device. In: International Conference on Computer and Communication Engineering, ICCCE 2008, pp. 818–822, May 2008
Batiz-Lazo, B., Reid, R.: The development of cash-dispensing technology in the UK. IEEE Annals of the History of Computing 33(3), 32–45 (2011)
Bradbury, D.: A hole in the security wall: ATM hacking. Network Security 2010(6), 12–15 (2010)
Bruzzone, L., Prieto, D.: Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing 38(3), 1171–1182 (2000)
Cooke, T.: Background subtraction using global textures. In: 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA), pp. 1–7, December 2012
Ding, N., Chen, Y., Zhong, Z., Xu, Y.: Energy-based surveillance systems for ATM machines. In: 2010 8th World Congress on Intelligent Control and Automation (WCICA), pp. 2880–2887, July 2010
Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)
Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Guo, H., Jin, B.: Forensic analysis of skimming devices for credit fraud detection. In: 2010 2nd IEEE International Conference on Information and Financial Engineering (ICIFE), pp. 542–546, September 2010
Hofmann, M., Tiefenbacher, P., Rigoll, G.: Background segmentation with feedback: the pixel-based adaptive segmenter. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 38–43, June 2012
Krivko, M.: A hybrid model for plastic card fraud detection systems. Expert Systems with Applications 37(8), 6070–6076 (2010)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)
Reardon, B., Nance, K., McCombie, S.: Visualization of ATM usage patterns to detect counterfeit cards usage. In: 2012 45th Hawaii International Conference on System Science (HICSS), pp. 3081–3088, January 2012
Sako, H., Watanabe, T., Nagayoshi, H., Kagehiro, T.: Self-defense-technologies for automated teller machines. In: International Machine Vision and Image Processing Conference, IMVIP 2007, pp. 177–184, September 2007
St-Charles, P.L., Bilodeau, G.A., Bergevin, R.: Flexible background subtraction with self-balanced local sensitivity. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 414–419, June 2014
Zin, T.T., Tin, P., Toriu, T., Hama, H.: A novel probabilistic video analysis for stationary object detection in video surveillance systems. IAENG International Journal of Computer Science 39(3), 295–306 (2012)
Yi, H., Liu, J., Wang, X.: Automatic alarm system for self-service bank based on image comparation. In: 2011 International Symposium on Computer Science and Society (ISCCS), pp. 48–50, July 2011
Yi, M.: Abnormal event detection method for ATM video and its application. In: Lin, S., Huang, X. (eds.) CESM 2011, Part II. CCIS, vol. 176, pp. 186–192. Springer, Heidelberg (2011)
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Rattanapitak, W., Wangsiripitak, S. (2015). Vision-Based System for Automatic Detection of Suspicious Objects on ATM. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_48
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DOI: https://doi.org/10.1007/978-3-319-23192-1_48
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